Machine Vision
BASIC DATA
course listing
A - main register
course code
EEM0040
course title in Estonian
Masinnägemine
course title in English
Machine Vision
course volume CP
-
ECTS credits
6.00
to be declared
yes
fully online course
not
assessment form
Graded assessment
teaching semester
autumn
language of instruction
Estonian
English
Study programmes that contain the course
code of the study programme version
course compulsory
EAMM23/25
yes
IAFM21/24
no
MAHM02/22
no
Structural units teaching the course
EE - Department of Electrical Power Engineering and Mechatronics
Course description link
Timetable link
View the timetable
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Anda teadmised masinnägemise kui valdkonna sisust, arengutest ja tulevikutrendidest.
Anda teadmised ja oskused masinnägemissüsteemi komponentide (kaamerad, sensorid, sardsüstteemid, tarkvara (LabView, Matlab ja OpenCV) jms.) kasutamisest.
Anda teadmised ja oskused tehnosüsteemide tüüpilisemate masinnägemise probleemide lahendamiseks ning masinnägemise süsteemide kavandamiseks arukates süsteemides.
Anda teadmised tehisintellekti ja masinõppe algoritmide rakendamisest kaasaegsetes masinnägemissüsteemides robootika ja arukate süsteemide arendusel.
course aims in English
To give knowledge about machine vision as a technology field, respective developments and future trends.
To give knowledge and skills to utilize machine vision system components (cameras, sensors, embedded vision elements, software (LabView, Matlab and OpenCV) etc.) .
To give knowledge and skills for solving typical machine vision problems in engineering systems and for developing respective machine vision solutions in smart systems.
To give knowledge about the use of AI and machine learning algorithms in advanced machine vision systems at robotics and smart system development.
learning outcomes in the course in Est.
Tunneb ja orienteerub kõrgtehnoloogiliste masinnägemissüsteemide põhiprintsiipides ning struktuuris ja nende süsteemide kasutamises mehhatroonika, robottehnika ja tootmissüsteemides
Tunneb peamisi masinnägemissüsteemides kasutatavaid tehnikaid ja meetodeid ning nende rakendatavust erinevates tehnilistes süsteemides
Tunneb ja on võimeline kasutama masinnägemissüsteemide kavandamise ja analüüsi kontseptsioone praktiliste süsteemide loomisel tehnoloogiliste ülesannete lahendamiseks.
Tunneb ja on võimeline rakendama erinevaid tarkvaralahendusi visuaalinformatsioonil baseeruvate otsustusprotsesside automatiseerimisel.
learning outcomes in the course in Eng.
Knows and orients in basic principles and structures of high-tech machine vision systems and their use in mechatronics, robotics and production systems.
Knows the main techniques and methods for machine vision applications and their usability in technology applications.
Knows and is able to use design and analysis concepts for developing practical machine vision systems for solving basic related technical problems.
Knows and is able to implement machine vision software to automate technology processes on the base of visual information.
brief description of the course in Estonian
Masinnägemistehnoloogia üldpõhimõtted ja kasutamine masinnägemise rakendustes; süsteemide parameetrid; optilised elemendid, tundlikud elemendid ja visuaalsete andmete hõive; biobaseeruvad optilised andurid ja muundurid; masinnägemise roll mehhatroonika ja automaatika rakendustes; masinnägemissüsteemid ja tööstus; masinnägemise kontseptsioonid ja algoritmid aukates süsteemides; kujutise saamine; kujutise konversioon; mustrid ja konvolutsioon; reaalajaline tunnuste ekstraheerimine ja tuvastamine; tunnuste valik ja plaanimine visuaalseks juhtimiseks; pilditöötlus ja otsustusprotsess; optilised monitooringu meetodid; robotite visuaalne juhtimine ja õpetamine; 3D masinnägemise tehnikad; masinnägemise rakenduste hindamine, analüüs ja realiseerimine; alternatiivlahendused ja trendid. Tehisintellekti ja masinõppe kasutamine masinnägemissüsteemides. Masinnägemissüsteemides kasutatav tarkvara (Matlab, LabView, OpenCV jt.). Kursus sisaldab ühe praktilise probleemi lahendamist kursuseprojektina.
brief description of the course in English
Understanding Machine Vision technology applications; Characteristics of Systems; Vision System Elements, Sensors, and data acquisition; Biological-Based Optical Sensors and Transducers; Machine Vision Importance for Real Mechatronic Applications and Automation; Machine Vision Industry; Machine Vision Concepts and algorithms; Image Acquisition; Image Conversion; Optical Information Processing and Pattern Recognition; Real-Time Feature Extraction and Image Recognition; Feature Selection and Planning for Visual Servoing; Image Processing and Decision-Making; Visual Methods for Monitoring and Detecting; Visual Guidance for Robots; Three-Dimensional Machine Vision Techniques; Evaluating Machine Vision Applications; Application Analysis and Implementation; Alternatives to Machine Vision and trends. Use of AI and Machine Learning at Machine Vision problem solving.
type of assessment in Estonian
Praktilised ülesanded: 20%
Kursuseprojekt: 50%
Teooria test: 30%
Arvestuse eelduseks on positiivselt arvestatud praktiliste ülesannete aruanded ja kaitstud projekt.
type of assessment in English
Practical exercises: 20%
Course project: 50%
Test: 30%
Pre-requirement for the exam is accepted exercise reports and presented course project report.
independent study in Estonian
Iseseisva töö eesmärgid:
kinnistada loengute materjali iseseisva mõtestatud tegevusega,
omandada vilumusi tööstuslike masinnägemissüsteemide praktiliste kavandamis- ja integreerimisülesannete lahendamisel,
omandada vilumusi inseneritegevuse kirjalike aruannete koostamisel ja praktilise tegevuse esitamisel.
independent study in English
The aim of individual work:
to deepen the knowledge developed by lecture material by individual analysis and practical work,
to develop skills for solving industrial machine vision systems practical problems,
to develop skills for composing written engineering reports and presenting practical developments.
study literature
Loengukonspekt (lecture slides and exrecise materials)
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
1.0
lectures
-
practices
2.0
practices
-
exercises
1.0
exercises
-
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus or link to Moodle or to home page
2025/2026 autumn
Daniil Valme, EE - Department of Electrical Power Engineering and Mechatronics
English
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    2024/2025 autumn
    Daniil Valme, EE - Department of Electrical Power Engineering and Mechatronics
    English
      2023/2024 autumn
      Daniil Valme, EE - Department of Electrical Power Engineering and Mechatronics
      English
        2022/2023 autumn
        Dhanushka Chamara Liyanage, EC - Kuressaare College
        English
          EEM0040 EvaluationCriteria.pdf 
          2021/2022 autumn
          Mart Tamre, EE - Department of Electrical Power Engineering and Mechatronics
          English
            EEM0040 EvaluationCriteria.pdf 
            2020/2021 autumn
            Mart Tamre, EE - Department of Electrical Power Engineering and Mechatronics
            English
              EEM0040 EvaluationCriteria.pdf 
              2019/2020 autumn
              Mart Tamre, EE - Department of Electrical Power Engineering and Mechatronics
              English
                EEM0040 EvaluationCriteria.pdf 
                Course description in Estonian
                Course description in English